5 papers
From Relative Entropy to Minimax: A Unified Framework for Coverage in MDPs
Xihe Gu, Urbashi Mitra, Tara Javidi
Targeted and deliberate exploration of state--action pairs is essential in reward-free Markov Decision Problems (MDPs). More precisely, different state-action pairs exhibit differe…
Partially Decentralized Multi-Agent Q-Learning via Digital Cousins for Wireless Networks
Talha Bozkus, Urbashi Mitra
Q-learning is a widely used reinforcement learning (RL) algorithm for optimizing wireless networks, but faces challenges with large state-spaces. Recently proposed multi-environmen…
Coverage Analysis for Digital Cousin Selection -- Improving Multi-Environment Q-Learning
Talha Bozkus, Tara Javidi, Urbashi Mitra
Q-learning is widely employed for optimizing various large-dimensional networks with unknown system dynamics. Recent advancements include multi-environment mixed Q-learning (MEMQ)…
A Multi-Agent Multi-Environment Mixed Q-Learning for Partially Decentralized Wireless Network Optimization
Talha Bozkus, Urbashi Mitra
Q-learning is a powerful tool for network control and policy optimization in wireless networks, but it struggles with large state spaces. Recent advancements, like multi-environmen…
Coverage Analysis of Multi-Environment Q-Learning Algorithms for Wireless Network Optimization
Talha Bozkus, Urbashi Mitra
Q-learning is widely used to optimize wireless networks with unknown system dynamics. Recent advancements include ensemble multi-environment hybrid Q-learning algorithms, which uti…